Water sorption hysteresis in wood: II mathematical modeling – functions beyond data fitting
Bibliographic record
Abstract
Abstract The Preisach model, the mathematical form of the independent domain model, has been used to describe water sorption hysteresis in wood for over 20 years, however, its geometric interpretation has not been fully explored. In this work, it is demonstrated that the geometric interpretation can be used to explain the five experimental hysteresis patterns identified in the first paper of this series. Additionally, a modification to the aforementioned model is suggested that involves a numerical implementation, which avoids the use of unknown parameters. Our approach was evaluated at 25 and 40°C by comparing the predicted 1st to 4th order scanning curves with experimental data for Douglas-fir, western red cedar and Aspen. The low prediction errors and well-maintained wiping-out property support the suitability of our approach. Compared to other models found in literature, the presented model has the advantage of high accuracy and easy implementation due to its nonparametric nature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".